Creativity and Autonomy in Swarm Intelligence Systems

This work introduces two swarm intelligence algorithms -- one mimicking the behaviour of one species of ants (\emph{Leptothorax acervorum}) foraging (a `Stochastic Diffusion Search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a `Particle Swarm Optimiser', PSO) -- and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploliting an artistic tension between the local behaviour of the `birds flocking' - as they seek to follow the input sketch - and the global behaviour of the `ants foraging' - as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putative `creativity' of this hybrid swarm system in the philosophical light of the `rhizome' and Deleuze's well known `Orchid and Wasp' metaphor

Data assurance in opaque computations

The chess endgame is increasingly being seen through the lens of, and therefore effectively defined by, a data ‘model’ of itself. It is vital that such models are clearly faithful to the reality they purport to represent. This paper examines that issue and systems engineering responses to it, using the chess endgame as the exemplar scenario. A structured survey has been carried out of the intrinsic challenges and complexity of creating endgame data by reviewing the past pattern of errors during work in progress, surfacing in publications and occurring after the data was generated. Specific measures are proposed to counter observed classes of error-risk, including a preliminary survey of techniques for using state-of-the-art verification tools to generate EGTs that are correct by construction. The approach may be applied generically beyond the game domain

The evolutionary roots of creativity: mechanisms and motivations

Funding: MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland). MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.We consider the evolution of cognition and the emergence of creative behaviour, in relation to vocal communication. We address two key questions: (i) what cognitive and/or social mechanisms have evolved that afford aspects of creativity?; (ii) has natural and/or sexual selection favoured human behaviours considered ‘creative’? This entails analysis of ‘creativity’, an imprecise construct: comparable properties in non-humans differ in magnitude and teleology from generally agreed human creativity. We then address two apparent problems: (i) the difference between merely novel productions and ‘creative’ ones; (ii) the emergence of creative behaviour in spite of high cost: does it fit the idea that females choose a male who succeeds in spite of a handicap (costly ornament); or that creative males capable of producing a large and complex song repertoire grew up under favourable conditions; or a demonstration of generally beneficial heightened reasoning capacity; or an opportunity to continually reinforce social bonding through changing communication tropes; or something else? We illustrate and support our argument by reference to whale and bird song; these independently evolved biological signal mechanisms objectively share surface properties with human behaviours generally called ‘creative’. Studying them may elucidate mechanisms underlying human creativity; we outline a research programme to do so.PostprintPeer reviewe

Four PPPPerspectives on Computational Creativity in theory and in practice

Computational creativity is the modelling, simulating or replicating of creativity computationally. In examining and learning from these `creative systems', from what perspective should the creativity of a system be considered? Are we interested in the creativity of the system's output? Or of its creative processes? Features of the system? Or how it operates within its environment? Traditionally computational creativity has focused more on creative systems' products or processes, though this focus has widened recently. Creativity research offers the Four Ps of creativity: Person/Producer, Product, Process and Press/Environment. This paper presents the Four Ps, explaining each in the context of creativity research and how it relates to computational creativity. To illustrate the usefulness of the Four Ps in taking broader perspectives on creativity in its computational treatment, the concepts of novelty and value are explored using the Four Ps, highlighting aspects of novelty and value that may otherwise be overlooked. Analysis of recent research in computational creativity finds that although each of the Four Ps appears in the body of computational creativity work, individual pieces of work often do not acknowledge all Four Ps, missing opportunities to widen their work's relevance. We can see, though, that high-status computational creativity papers do typically address all Four Ps. This paper argues that the broader views of creativity afforded by the Four Ps is vital in guiding us towards more comprehensively useful computational investigations of creativity. This paper is available for free download during September 2016 at http://tandfonline.com/doi/full/10.1080/09540091.2016.115186

On the role of computers in creativity-support systems

We report here on our experiences with designing computer-based creativity-support systems over several years. In particular, we present the design of three different systems incorporating different mechanisms of creativity. One of them uses an idea proposed by Rodari to stimulate imagination of the children in writing a picture-based story. The second one is aimed to model creativity in legal reasoning, and the third one uses low-level perceptual similarities to stimulate creation of novel conceptual associations in unrelated pictures.We discuss lessons learnt from these approaches, and address their implications for the question of how far creativity can be tamed by algorithmic approaches

Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10462-016-9505-7.The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration.I thank the organisers of the AEPIA Summer School On Artificial Intelligence, held in September 2014, for giving me the opportunity to give a lecture on 'AI Evaluation'. This paper was born out of and evolved through that lecture. The information about many benchmarks and competitions discussed in this paper have been contrasted with information from and discussions with many people: M. Bedia, A. Cangelosi, C. Dimitrakakis, I. GarcIa-Varea, Katja Hofmann, W. Langdon, E. Messina, S. Mueller, M. Siebers and C. Soares. Figure 4 is courtesy of F. Martinez-Plumed. Finally, I thank the anonymous reviewers, whose comments have helped to significantly improve the balance and coverage of the paper. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grants TIN 2013-45732-C4-1-P, TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEOII2015/013.José Hernández-Orallo (2016). Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement. 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Comparing political futures: the rise and use of scenarios in future-oriented area studies

The predictive ability of scholars of politics has long been a subject of theoretical debate and methodological development. In theoretical debate, prediction represents a central issue regarding the extent to which the study of politics is scientific. In methodological development, much effort and resource have been devoted to a diverse range of predictive approaches, with varying degrees of success. Expectations that scholars forecast accurately come as much from the policy and media worlds as from the academy. Since the end of the Cold War, scenario development has become prevalent in future-oriented research by area studies scholars. This approach is long due critical re-assessment. For all its strengths as a policy tool, scenario development tends towards a bounded methodology, driving the process of anticipating futures along predetermined paths into a standardised range of options, and paying insufficient attention to theoretical and contextual understandings available within the relevant scholarly disciplines

The Fifth Generation: Artificial Intelligence and Japan\u27s Computer Challenge to the World

Knowledge is the future power and Japan wants to be the first in developing and marketing the Fifth Generation of computers. What is The Fifth Generation? Why Japan ? and how would it affect the Western world